Remote Sensing Images (RSIs) often have extremely wide width and abundant terrain.In order to achieve rapid object detection in large RSIs, in this paper, a Deep Hash Assisted Network (DHAN) is constructed by introducing a hashing encoding of images in a two-stage deep neural network.Different with the available detection networks, DHAN first locates candidate object regions and then transfers the learned features to another att nighthawk hotspot Region Proposal Network (RPN) for detection.On the one hand, it can avoid the calculations on the background irrelevant to objects.
On the other hand, the built hash encoding layer in DHAN can accelerate the detection via binary hash features.Moreover, a self attention layer is designed and combined with the convolution layer, to distinguish relatively u11-200ps small objects regions from a very large scene.The proposed method is tested on several public data sets, and the comparison results show that DHAN can remarkably improve the detection efficiency on large RSIs and simultaneously achieve high detection accuracy.